Project Summary/Abstract The main goal of this study is to build a data-driven, evidence-based organizational management system that can inform effective recruitment and retention strategies to prevent excessive turnover. High turnover rates (estimated 25-60% annually) are devastating for mental health care systems, affecting organizations (e.g., cost), employees (e.g., work well-being), and most critically, the quality of care. Human resource departments collect extensive employee data that can be useful predictors for turnover, but these data are not often analyzed to address turnover issues in mental health organizations. Computational methods have greatly evolved and can now access and analyze large and complex data. This pilot study will achieve three specific aims: Aim 1: build and test turnover prediction models by developing and applying machine learning algorithms to existing human resource data; Aim 2: generate critical questions to enhance turnover prediction through qualitative methods; and Aim 3: test the enhanced model in predicting turnover at 12 months. In Aim 1, using past human resource data and service encounters from [two mental health organizations (rural and urban locations)], we will develop machine learning algorithms to predict turnover. The algorithms will address turnover questions simultaneously (e.g., Who are the most likely to leave? What factors predict turnover at varying time points in employment?). In Aim 2, we will interview key informants: ?leavers? (employees who voluntarily terminate employment during the study); ?stayers? (employees with extreme longevity in the organization); and ?predictees? (identified as likely to leave, based on our algorithms). The findings will be discussed in two focus groups in order to generate, refine, and validate 5-10 critical questions to enhance prediction of turnover. In Aim 3, we will conduct an on-line survey of all current employees to assess the 5-10 critical questions and link survey data with data from human resources and services to examine the improved precision between the theory-based model (predictors in the survey) and the data-driven model (machine learning algorithms) in predicting actual turnover 12 months later. Machine learning can model complex and dynamic variable relationships (e.g., handling a large number of variables, accounting for heterogeneity) and overcome limitations in traditional turnover research that often relies on small, cross-sectional, and convenience samples. Successful completion of this study will promote data-driven, evidence-based organizational management practices to address turnover, which is aligned with NIMH priorities of capitalizing on existing data structures and using technologies to improve mental health service quality. This study will be a critical step in developing highly adaptable machine learning algorithms to predict turnover; ultimately, we envision that this system will be partnered with future clinical interventions to reduce turnover in mental health.